7 accepted papers
Many robot tasks impose constraints on the workspace. For example, a robot may need to move a container without spilling its contents or open a door following the doorknob’s arc. Such constraints may induce narrow volumes in the configuration space, traditionally a challenge for sampling-based metho…
Sampling-based motion planning works well in many cases but is less effective if the configuration space has narrow passages. In this paper, we propose a learning-based strategy to sample in these narrow passages, which improves overall planning time. Our algorithm first learns from the configuratio…
Achieving a complete motion planner that guarantees a plan or infeasibility proof in finite time is challenging, especially in high-dimensional spaces. Previous efforts have introduced asymptotically complete motion planners capable of providing a plan or infeasibility proof given long enough time.
Acceptance of social robots in human-robot collaborative environments depends on the robots’ sensitivity to human moral and social norms. Robot behavior that violates norms may decrease trust and lead human interactants to blame the robot and view it negatively. Hence, for long-term acceptance, soci…
We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based motion planners are effective in high-dimensional spaces but are only probabilistically complete. Consequently; these planners cannot provide a definite answer if no plan exists; which i…
We present a general approach for constructing proofs of motion planning infeasibility. Effective high-dimensional motion planners, such as sampling-based methods, are limited to probabilistic completeness, so when no plan exists, these planners either do not terminate or can only run until a timeou…
Robot teleoperation is a transformative field that can enable workers to safely perform tasks in dangerous environments. In this letter, we present our work towards a teleoperation system with safe, realistic force feedback for intuitive control of a robotic arm and anthropomorphic robotic hand as i